Correlating consumption and voltage data to identify line loss in an electric grid

ABSTRACT

Systems, methods, and other embodiments associated with identifying non-technical line loss using data from smart meters in an electric grid are described. In one embodiment, a method includes querying a utility database to collect meter data, wherein the meter data is from electric meters connected to a transformer in an electric grid. Querying the utility database includes collecting the data according to a plurality of intervals over a period of time. Electric consumption and voltage variances are analyzed for the set of meters to identify a first set of intervals that satisfy a threshold for electric consumption and to identify a second set of intervals that satisfy a threshold for voltage variances. The first set of intervals is compared with the second set of intervals to determine whether the set of meters are associated with non-technical line loss.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent disclosure is a continuation of U.S. patent application Ser.No. 14/525,307 filed Oct. 28, 2014, which is hereby wholly incorporatedby reference.

BACKGROUND

For electric utility companies, line loss can be a major source of lostrevenue. Line loss is the loss of electricity during transmission anddistribution through an electric grid. In general, there are two typesof line loss. Technical line loss is associated with loss of electricityfrom physical properties of the metal wires and the electric grid.Non-technical line loss is caused by theft of electricity or othernon-technical sources such as clerical errors. Determining locations fora source of line loss can be difficult.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various systems, methods, andother embodiments of the disclosure. It will be appreciated that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one embodiment of the boundaries. Insome embodiments, one element may be designed as multiple elements ormultiple elements may be designed as one element. In some embodiments,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale.

FIG. 1 illustrates one embodiment of a system associated withidentifying sources of line loss by using smart meter data.

FIG. 2 illustrates one embodiment of a method associated with analyzingvoltage and consumption data from electric meters to identify sources ofline loss.

FIG. 3 illustrates an embodiment of a computing system configured withthe example systems and/or methods disclosed.

DETAILED DESCRIPTION

Systems and methods are described herein that provide for identifyinglikely sources of non-technical line loss according to correlationsbetween consumption and voltage fluctuations. In one embodiment, utilitydata is collected for a set of electric smart meters associated with atransformer. The utility data includes, for example, informationcollected on at least an hourly basis for each electric meter connectedto the transformer. In general, the utility data includes an hourlyaverage voltage for each meter and hourly consumption data for eachmeter.

The voltage and the consumption data for a meter should correlate. Forexample, a lower voltage is indicative of a higher consumption for aparticular meter. This is because as a load on the meter draws electricpower the voltage at the meter may drop below a standard voltage.Therefore, when a meter with a lower voltage does not correspond with ahigher consumption, non-technical line loss (i.e., electricity theft) islikely occurring. The meter may be identified and flagged so thatfurther investigation can be performed. In this way, data that includesvoltage variances and electricity consumption can be correlated todetermine whether the utility data indicates a presence or possibilityof non-technical line loss. This is an improvement over conventionalsystems which are not able to identify these types of conditions and donot perform the present novel technique.

With reference to FIG. 1, one embodiment of a computing system 100associated with identifying sources of line loss is illustrated. Thesystem 100 includes a utility analysis system 105 configured as anexecutable application that includes at least analysis logic 110 anddetect logic 120. In one embodiment, the analysis logic 110 isconfigured to collect data from a utility database 130 that issubsequently correlated by the detect logic 120 to identify whether lineloss is present or at least a suspicion of line loss may be occurring.

In general, the system 100 is a computer system that includes at least aprocessor, memory, and network interface, and includes the utilityanalysis system that is configured to communicate with a number of smartmeters A-N over a communication network 140. The smart meters, forexample, are configured in various remote locations and operate in anelectric grid with at least one transformer. The computer system 100collects meter data from smart meters and stores the data in the utilitydatabase 130 in data structures such as data records, data tables,files, etc. for each smart meter. The electric grid is an electricdistribution network of a utility company. Accordingly, the electricgrid includes many different components. The different componentsconnect from a power generation station down to meters at each locationof service (e.g., residential location) to provide electricity.Generally speaking, a transformer in the electric grid provideselectricity to a set of meters that may include from one to hundreds ofmeters.

The meters are, for example, smart meters that are connected to powerlines. The smart meters are configured to monitor and log data aboutelectric usage over the power lines. In one embodiment, the metersprovide information about electric usage on-demand via network messagesto the system 100. Accordingly, the meters may provide continuous dataabout electric usage, data at some predefined interval, or data whenrequested. In either case, the information may be provided from themeters at a rate which is most relevant to a particular implementation.The information stored in the utility database 130 is collected toprovide an accurate representation of consumption and voltage values fora granularity of a time interval at which analysis of the informationwill be undertaken.

For example, the information stored in the database 130 includes bothelectric consumption data and voltage data. The electric consumptiondata indicates how much electricity has been consumed by a load attachedto a meter. In one embodiment, the electric consumption data for eachmeter is provided at an interval of an hour. In this way, enough data isavailable to provide an accurate portrait of consumption while notover-burdening storage. However, in another embodiment, the consumptiondata may be provided at an interval of the minute, half hour, day and soon.

With respect to the data itself, the voltage data is data for each meterthat indicates a current voltage present at the meter. In oneembodiment, the voltage data may be an average voltage for an intervalof time (e.g., one hour). Accordingly, the voltage data may be expressedas a mean for the interval of time (e.g., root mean square (RMS)) forthe interval or whichever form is most applicable to a given interval.Of course, the data may be organized and formatted in other desiredformats using different types of data structures and may include avariety data attributes/properties that are collected and stored.

After some amount of utility data is collected in the utility database130, an analysis of the data can be performed, for example, in responseto a request or a trigger condition (e.g., time interval, etc.). Whenthe analysis initiates, the analysis logic 110 is configured tocollect/retrieve the data from the utility database 130 and then analyzethe data. In one embodiment, the analysis logic 110 is configured toquery the utility database 130 using electronic communications over acommunication network. The query may specify a period of time (e.g., oneweek, one month, etc.) for providing the data and also at which intervalor granularity to provide the data (e.g., hourly, daily, etc.).

In one embodiment, the analysis logic 110 receives the data in responseto the query and divides the data into appropriate intervals foranalysis. Furthermore, the analysis logic 110 requests data related to,for example, meters of a single transformer. Thus, the data collected bythe analysis logic 110 includes electric consumption and voltage datafor however many meters are connected to the transformer.

Once the data is available, the analysis logic 110 begins analyzing thedata by determining voltage variances and electric consumption totalsfor each interval.

Voltage Variances

In one embodiment, the analysis logic 110 is configured to determinevoltage variances from the utility data by calculating a variance foreach meter at each interval of time for a period. The analysis logic 110first determines a maximum voltage among the set of meters for eachinterval during the period of time (i.e., for each hour of a week). Themaximum voltage for each interval is a reference voltage for thatrespective interval. As one example, the reference voltage will be astandard line voltage that is supplied to each meter by the transformer(e.g., 120 volts).

The analysis logic 110 uses the reference voltage for each interval tocalculate a difference between the reference voltage for an interval andvoltages of the meters for the same interval. The difference for eachmeter in the interval is a voltage variance for that particular meterand that particular interval. The analysis logic 110 calculates thevoltage variances across all intervals of the period of time for eachmeter. Thus, in an example where there are ten meters for thetransformer, there will be ten separate voltage variances for eachinterval.

The voltage variances indicate a variation from the reference voltage.When a voltage variance exists it is generally a function of consumptionat a meter exhibiting the variance. That is, when an electric load isattached to the meter, the load will cause a voltage at the meter to bepulled down by a number of volts relative to the load (e.g., 5-10volts). Additionally, when a meter is being bypassed because ofelectricity theft (i.e., non-technical line loss), the meter willexhibit a voltage variance while not registering a correlating electricconsumption. Therefore, the voltage variance for a meter can be used asan indicator of electricity theft when correlated with electricconsumption. The system 105 may generate a signal or other message thatidentifies meter as a candidate meter for further investigation.

In one embodiment, once the analysis logic 110 has calculated thevoltage variances for each interval over the period of time, theanalysis logic 110 proceeds by determining which of the intervals havevoltage variances that satisfy a threshold voltage condition. Theanalysis logic 110, for example, sorts the voltage variances for theperiod of time according to greatest variation to lowest variation. Theanalysis logic 110 then analyzes the sorted voltage variances todetermine which of the intervals satisfy the threshold voltagecondition.

In one embodiment, the threshold voltage condition is a function of apercentage among all of the intervals. That is, the analysis logic 110determines that a particular interval satisfies the threshold voltagecondition when an interval is within a specified percentage for voltagevariances among the intervals. For example, an interval satisfies thevoltage threshold condition when the interval is in a top decile (i.e.,top 10%) of intervals for voltage variances. That is, the interval has agreater amount of variances than ninety percent of the intervals as awhole. The analysis logic 110 identifies intervals that satisfy thethreshold voltage condition and groups those intervals into a first setof intervals. Of course, while a top decile is discussed, in otherembodiments, a different voltage threshold condition may be implemented.For example, a top 5 percent or a top 20 percent.

Electric Consumption

In one embodiment, the analysis logic 110 is configured to determineelectric consumption for each interval from the utility data. Theanalysis logic 110 sums electric consumption for each interval toprovide a total electric consumption for each interval over the periodof time. In one embodiment, the analysis logic 110 calculates a totalelectric consumption for each interval by summing an electricconsumption from each of the meters for a respective interval. As aresult, the analysis logic 110 provides a total consumption ofelectricity for each of the intervals over the period of time. Ingeneral, the total consumption for each of the intervals is recorded in,for example, kilowatt hours (kWh).

Furthermore, the analysis logic 110 is configured to, for example,determine which of the intervals satisfy a consumption thresholdcondition. In one embodiment, the analysis logic 110 sorts the intervalsaccording to a total consumption from the meters for each interval.Accordingly, the analysis logic 110 generates a record of intervals witha highest consumption to intervals with a lowest consumption.

The analysis logic 110 further determines which of the intervals satisfythe threshold consumption condition. In one embodiment, the thresholdconsumption condition is a top decile of intervals with the mostconsumption among all of the intervals for the period of time. That is,the analysis logic 110 determines an interval satisfies the thresholdconsumption condition if an interval is in a top 10 percent of theintervals for electric consumption. If an interval satisfies thethreshold consumption condition, then the analysis logic 110 groups theinterval into a second set of intervals to record the interval assatisfying the consumption threshold condition. In this way, theanalysis logic 110 may determine which of the intervals over the periodof time have a highest electric consumption.

Line Loss

Once the intervals associated with voltage variances and electricconsumption have been determined, the detect logic 120, in oneembodiment, correlates the two set of intervals to determine whethernon-technical line loss is present among the meters.

The detect logic 120 is configured to, for example, compare the firstset of intervals with the second set of intervals. Accordingly, bycomparing the intervals with a top consumption and a top voltagevariance, the detect logic 120 is determining whether voltagefluctuations among the meters correlates with consumption. If the twosets of intervals do not correlate, then electricity theft is likelypresent and the meters are flagged for further investigation. In oneembodiment, the detect logic 120 determines whether the two sets ofintervals correlate by determining whether at least a certain predefinedmargin/percentage (e.g., 90%) of the intervals with a top voltagevariance are also in the intervals with a top electric consumption.

In general, the percentage of correlation may differ depending on aparticular implementation; however, when the two sets of intervals donot correlate there is a high likelihood of electricity theft among themeters. Accordingly, the detect logic 120 generates an electronicnotification to indicate that the meters should be further investigatedfor theft. In one embodiment, the detect logic 120 generates a note withthe associated transformer and/or each of the meters in the utilitydatabase 130 to note the finding.

Further aspects of identifying sources of electricity theft in anelectric grid will be discussed with reference to FIG. 2. FIG. 2illustrates a method 200 associated with detecting electricity theftusing and analyzing data from smart meters.

At 210, utility data is collected/retrieved from a utility database. Inone embodiment, the utility database is an electronic database of autility company that includes information about components in anelectric grid of the utility company. The information (e.g.,utility/meter data) is generally collected from smart meters and otherpoints within the electric grid to track and record conditions in theelectric grid and electric use associated with the smart meters. In oneembodiment, the utility data for a particular analysis iscollected/retrieved by querying the utility database about electricmeters connected to a selected transformer in the electric grid. Theutility data is generally collected for an analysis of the electricmeters according to a plurality of intervals over a period of time formeters of a single transformer.

Additionally, the utility data includes electric consumption data andvoltage data that is divided into the plurality of intervals. Thus, datafor a particular set of meters can be collected and provided at aspecified granularity (e.g., hour, day, week, etc.) apart from bulk datain the utility database for the whole electric grid.

At 220, voltage variances are determined from the utility data for themeters according to each of the plurality of intervals. In oneembodiment, a reference voltage for each of the plurality of intervalsis first determined. The reference voltage at each interval is, forexample, a voltage of a meter that is a maximum voltage among the metersfor that interval. In general, it is expected that the reference voltagewould be a standard line voltage (e.g., 120V or 240V). However, as eachtransformer varies slightly in physical properties and as differentoperating conditions occur (e.g., weather, load, etc.) the referencevoltage may vary from the standard line voltage.

Subsequently, differences between a reference voltage for each of theintervals and mean (e.g., RMS′/Actual′ as coming from the meter)voltages for each of the meters at each of the intervals are calculated.As explained previously, variances (i.e., voltage fluctuations) from thereference voltage generally occur when electric consumption at anassociated meter is high.

At 230, time intervals that satisfy a voltage threshold are identified.In one embodiment, to identify the time intervals that satisfy thevoltage threshold, the voltage variances are first sorted into a list inorder of, for example, largest to smallest variances. Thereafter, theintervals that satisfy the voltage threshold can be determined byanalyzing the voltage variances. In one embodiment, intervals that arewithin a predefined percentage among the plurality of intervals forlargest voltage variances satisfy the voltage threshold and are recordedin a first set of intervals. That is, if an interval is within, forexample, a top ten percent in comparison to voltage variances of theplurality of intervals for the highest voltage variances, then theinterval satisfies the voltage threshold.

In one embodiment, at 230, one voltage variance is chosen per eachinterval with, for example a largest voltage variance of that interval.Subsequently, the selected voltage variances values are analyzed byordering the variances, to determine which intervals have a largestvoltage variance (e.g., surpass a voltage variance threshold).

At 240, electric consumption totals are determined for each of theplurality of intervals. In one embodiment, determining the electricconsumption totals includes summing electric consumption for each of theplurality of intervals. That is, all consumption for the meters is addedtogether at each interval to define totals for the plurality ofintervals. In this way, interval specific totals for electricconsumption can be determined.

At 250, time intervals that satisfy a consumption threshold areidentified. In one embodiment, the consumption threshold is a ranking orpercentage among the plurality of intervals for electric consumption.Thus, for a respective interval to satisfy the consumption thresholddepends on whether the interval is within the predefined percentage(e.g., top 10% for consumption). In this way, intervals with a highestconsumption can be recorded and listed in a second set of intervals forcomparison.

In general, the electric consumption totals should reflect totals forthe transformer associated with the meters. However, transformersthemselves are not generally metered. That is, a transformer does nothave an individual meter to records how much electricity is providedthrough the transformer. Instead, each location (i.e., residence)attached to the transformer is metered. Accordingly, at 260, theelectric consumption data and voltage variances are analyzed todetermine whether the consumption for the set of meters correlates withthe voltage variances. Because consumption data is not available fortransformers to simply compare with totals from connected meters,correlating voltage variances with individual meter consumption caninstead provide an indicator of likely sources of theft.

Thus, at 260, the first set of intervals (i.e., largest voltagevariance) is compared with the second set of intervals (i.e., largestconsumption) to determine whether the two sets correlate (i.e., match).

At 270, the set of meters is identified as likely includingnon-technical line loss (i.e., electricity theft) when the first set ofintervals and the second set of intervals do not substantiallycorrelate. A signal or message can be generated to indicate theidentified meter(s) and the suspicion of electricity theft. In oneembodiment, in order to qualify as not substantially correlating 90% ofthe top decile of intervals with voltage variations (i.e., the firstset) are not in the intervals of top decile of intervals for electricconsumption (i.e., the second set), then the meters associated with thetransformer likely include one or more meters where theft is occurring.

Accordingly, records for the meters may be updated in the utilitydatabase to reflect the presence of theft. Furthermore, method 200 maybe repeated for other transformers in the electric grid as varioustransformers are selected or according to some other function.

Computer Embodiment

FIG. 3 illustrates an example computing device that is configured and/orprogrammed with one or more of the example systems and methods describedherein, and/or equivalents. The example computing device may be acomputer 300 that includes a processor 302, a memory 304, andinput/output ports 310 operably connected by a bus 308. In one example,the computer 300 is configured with the utility analysis logic 330configured to facilitate identifying electric meters associated withnon-technical line loss similar to the utility analysis system 105 asshown in the system 100 in FIG. 1. In different examples, the logic 330may be implemented in hardware, a non-transitory computer-readablemedium with stored instructions, firmware, and/or combinations thereof.While the logic 330 is illustrated as a hardware component attached tothe bus 308, it is to be appreciated that in one example, the logic 330could be implemented in the processor 302.

In one embodiment, logic 330 or the computer is a means (e.g., hardware,non-transitory computer-readable medium, firmware) for analyzing datafrom a utility database to determine whether electric consumptioncorrelates with voltage variances associated with a set of meters inorder to identify non-technical line loss.

The means may be implemented, for example, as an ASIC programmed toanalyze data from a utility database to determine whether electricconsumption correlates with voltage variances associated with a set ofmeters in order to identify non-technical line loss. The means may alsobe implemented as stored computer executable instructions that arepresented to computer 300 as data 316 that are temporarily stored inmemory 304 and then executed by processor 302.

Generally describing an example configuration of the computer 300, theprocessor 302 may be a variety of various processors including dualmicroprocessor and other multi-processor architectures. A memory 304 mayinclude volatile memory and/or non-volatile memory. Non-volatile memorymay include, for example, ROM, PROM, and so on. Volatile memory mayinclude, for example, RAM, SRAM, DRAM, and so on.

A storage disk 306 may be operably connected to the computer 300 via,for example, an input/output interface (e.g., card, device) 318 and aninput/output port 310. The disk 306 may be, for example, a magnetic diskdrive, a solid state disk drive, a floppy disk drive, a tape drive, aZip drive, a flash memory card, a memory stick, and so on. Furthermore,the disk 306 may be a CD-ROM drive, a CD-R drive, a CD-RW drive, a DVDROM, and so on. The memory 304 can store a process 314 and/or a data316, for example. The disk 306 and/or the memory 304 can store anoperating system that controls and allocates resources of the computer300.

The computer 300 may interact with input/output devices via the i/ointerfaces 318 and the input/output ports 310. Input/output devices maybe, for example, a keyboard, a microphone, a pointing and selectiondevice, cameras, video cards, displays, the disk 306, the networkdevices 320, and so on. The input/output ports 310 may include, forexample, serial ports, parallel ports, and USB ports.

The computer 300 can operate in a network environment and thus may beconnected to the network devices 320 via the i/o interfaces 318, and/orthe i/o ports 310. Through the network devices 320, the computer 300 mayinteract with a network. Through the network, the computer 300 may belogically connected to remote computers. Networks with which thecomputer 300 may interact include, but are not limited to, a LAN, a WAN,and other networks.

Definitions and Other Embodiments

In another embodiment, the described methods and/or their equivalentsmay be implemented with computer executable instructions. Thus, in oneembodiment, a non-transitory computer storage medium is configured withstored computer executable instructions that when executed by a machine(e.g., processor, computer, and so on) cause the machine (and/orassociated components) to perform the method.

While for purposes of simplicity of explanation, the illustratedmethodologies in the figures are shown and described as a series ofblocks, it is to be appreciated that the methodologies are not limitedby the order of the blocks, as some blocks can occur in different ordersand/or concurrently with other blocks from that shown and described.Moreover, less than all the illustrated blocks may be used to implementan example methodology. Blocks may be combined or separated intomultiple components. Furthermore, additional and/or alternativemethodologies can employ additional actions that are not illustrated inblocks. The methods described herein are limited to statutory subjectmatter under 35 U.S.C §101.

The following includes definitions of selected terms employed herein.The definitions include various examples and/or forms of components thatfall within the scope of a term and that may be used for implementation.The examples are not intended to be limiting. Both singular and pluralforms of terms may be within the definitions.

References to “one embodiment”, “an embodiment”, “one example”, “anexample”, and so on, indicate that the embodiment(s) or example(s) sodescribed may include a particular feature, structure, characteristic,property, element, or limitation, but that not every embodiment orexample necessarily includes that particular feature, structure,characteristic, property, element or limitation. Furthermore, repeateduse of the phrase “in one embodiment” does not necessarily refer to thesame embodiment, though it may.

“Computer communication”, as used herein, refers to a communicationbetween computing devices (e.g., computer, personal digital assistant,cellular telephone) and can be, for example, a network transfer, a filetransfer, an applet transfer, an email, an HTTP transfer, and so on. Acomputer communication can occur across, for example, a wireless system(e.g., IEEE 802.11), an Ethernet system (e.g., IEEE 802.3), a token ringsystem (e.g., IEEE 802.5), a LAN, a WAN, a point-to-point system, acircuit switching system, a packet switching system, and so on.

“Computer-readable medium”, as used herein, refers to a non-transitorymedium that stores instructions and/or data configured to perform one ormore of the disclosed functions when executed. A computer-readablemedium may take forms, including, but not limited to, non-volatilemedia, and volatile media. Non-volatile media may include, for example,optical disks, magnetic disks, and so on. Volatile media may include,for example, semiconductor memories, dynamic memory, and so on. Commonforms of a computer-readable medium may include, but are not limited to,a floppy disk, a flexible disk, a hard disk, a magnetic tape, othermagnetic medium, an application specific integrated circuit (ASIC), aprogrammable logic device, a compact disk (CD), other optical medium, arandom access memory (RAM), a read only memory (ROM), a memory chip orcard, a memory stick, and other media from which a computer, a processoror other electronic device can function with. Each type of media, ifselected for implementation in one embodiment, includes storedinstructions of an algorithm configured to perform one or more of thedisclosed and/or claimed functions.

“Logic”, as used herein, includes a component that is implemented incomputer or electrical hardware, firmware, a non-transitory medium withstored instructions of an executable algorithm/application, and/orcombinations of these to perform any of the functions or actions asdisclosed herein, and/or to cause a function or action from anotherlogic, method, and/or system to be performed as disclosed herein. Logicmay include a microprocessor programmed with an algorithm, a discretelogic (e.g., ASIC), at least one circuit, an analog circuit, a digitalcircuit, a programmed logic device, a memory device containinginstructions of an algorithm, and so on, all of which are configured toperform one or more of the disclosed functions. Logic may include one ormore gates, combinations of gates, or other circuit componentsconfigured to perform one or more of the disclosed functions. Wheremultiple logics are described, it may be possible to incorporate themultiple logics into one logic. Similarly, where a single logic isdescribed, it may be possible to distribute that single logic betweenmultiple logics. In one embodiment, one or more of these logics arecorresponding structure associated with performing the disclosed and/orclaimed functions. Choice of which type of logic to implement may bebased on desired system conditions or specifications. Logic is limitedto statutory subject matter under 35 U.S.C. §101.

An “operable connection”, or a connection by which entities are“operably connected”, is one in which signals, physical communications,and/or logical communications may be sent and/or received. An operableconnection may include a physical interface, an electrical interface,and/or a data interface. An operable connection may include differingcombinations of interfaces and/or connections sufficient to allowoperable control. For example, two entities can be operably connected tocommunicate signals to each other directly or through one or moreintermediate entities (e.g., processor, operating system, logic,non-transitory computer-readable medium). Logical and/or physicalcommunication channels can be used to create an operable connection.

While example systems, methods, and so on have been illustrated bydescribing examples, and while the examples have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe systems, methods, and so on described herein. Therefore, thedisclosure is not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Thus, thisdisclosure is intended to embrace alterations, modifications, andvariations that fall within the scope of the appended claims, whichsatisfy the statutory subject matter requirements of 35 U.S.C. §101.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

To the extent that the term “or” is used in the detailed description orclaims (e.g., A or B) it is intended to mean “A or B or both”. When theapplicants intend to indicate “only A or B but not both” then the phrase“only A or B but not both” will be used. Thus, use of the term “or”herein is the inclusive, and not the exclusive use.

What is claimed is:
 1. A non-transitory computer-readable medium storingcomputer-executable instructions that when executed by a processor of acomputer causes the processor to: retrieve data from a utility database,wherein the data specifies electric usage information from a set ofelectric meters that are associated with an electric grid; analyze thedata to determine a first set of time intervals with voltage variancesthat satisfy a voltage threshold condition; analyze the data todetermine a second set of time intervals that include electricconsumption that satisfy a consumption threshold condition; and comparethe first set of time intervals and the second set of time intervals toidentify whether the set of electric meters are associated withnon-technical line loss, wherein the non-technical line loss is detectedwhen the first set of time intervals of the voltage variances do notmatch at least a threshold percentage of the second set of timeintervals of the electric consumption within a defined margin.
 2. Thenon-transitory computer-readable medium of claim 1, wherein the datafrom the utility database specifies electric usage information over aperiod of time that is divided into a plurality of time intervals. 3.The non-transitory computer-readable medium of claim 2, wherein theinstructions for retrieving that data comprise instructions to: querythe utility database according to an electronic communication thatincludes a query specifying the period of time and an interval thatcorrelates the plurality of intervals.
 4. The non-transitorycomputer-readable medium of claim 2, wherein the instructions compriseinstructions to: determine voltage variances between a voltage for eachof the set of meters at the plurality of time intervals and a respectivereference voltage associated with each of the plurality of timeintervals.
 5. The non-transitory computer-readable medium of claim 4,wherein the voltage for each of the set of meters is a root mean square(RMS) voltage for a respective interval of the plurality of timeintervals.
 6. The non-transitory computer-readable medium of claim 4,wherein the respective reference voltage for each of the plurality oftime intervals is a maximum voltage at each interval of the plurality oftime intervals.
 7. The non-transitory computer-readable medium of claim2, wherein the instructions comprise instructions to: determine electricconsumption for the set of electric meters at each of the plurality oftime intervals by summing a total electric consumption for the set ofmeters during respective intervals of the plurality of time intervals.8. The non-transitory computer-readable medium of claim 7, wherein theelectric consumption for each of the set of meters is a number ofkilowatt hours (kWh) for each of the plurality of time intervals.
 9. Thenon-transitory computer-readable medium of claim 2, wherein theinstructions for determining the first set of time intervals compriseinstructions to: sort the voltage variances to determine which of theplurality of time intervals satisfy the voltage threshold condition,wherein the voltage threshold condition is satisfied when a timeinterval is ranked in a first decile for the voltage variances among theplurality of time intervals.
 10. A computing system, comprising: aprocessor connected to memory; and a module stored on a non-transitorycomputer readable medium and configured with instructions that whenexecuted by the processor cause the processor to: retrieve data from autility database, wherein the data specifies electric usage informationfrom a set of electric meters that are associated with an electric grid;analyze the data to determine a first set of time intervals with voltagevariances that satisfy a voltage threshold condition; analyze the datato determine a second set of time intervals that include electricconsumption that satisfy a consumption threshold condition; and comparethe first set of time intervals and the second set of time intervals toidentify whether the set of electric meters are associated withnon-technical line loss, wherein the non-technical line loss is detectedwhen the first set of time intervals of the voltage variances do notmatch at least a threshold percentage of the second set of timeintervals of the electric consumption within a defined margin.
 11. Thecomputing system of claim 10, wherein the data from the utility databasespecifies electric usage information over a period of time that isdivided into a plurality of time intervals.
 12. The computing system ofclaim 11, wherein the instructions for determining the second set oftime intervals comprise instructions to cause the processor to: sort theplurality of time intervals according to electric consumption todetermine which of the plurality of time intervals satisfy theconsumption threshold condition, wherein the consumption thresholdcondition is satisfied when a time interval of the plurality of timeintervals is ranked in a top decile for electric consumption among theplurality of time intervals.
 13. The computing system of claim 11,wherein the instructions for comparing the first set of time intervalsand the second set of time intervals comprise instructions to cause theprocessor to: correlate voltage fluctuations and electric consumptionamong the set of meters to identify irregularities consistent withelectricity theft.
 14. The computing system of claim 11, wherein theinstructions cause the processor to: determine voltage variances betweena voltage for each of the set of meters at the plurality of timeintervals and a respective reference voltage associated with each of theplurality of time intervals.
 15. The computing system of claim 11,wherein the instructions cause the processor to: determine electricconsumption for the set of electric meters at each of the plurality oftime intervals by summing a total electric consumption for the set ofmeters during respective intervals of the plurality of time intervals.16. A computer-implemented method, the computer-implemented methodinvolving a computing device comprising a processor, and thecomputer-implemented method comprising: retrieving, by the processor,data from a utility database, wherein the data specifies electric usageinformation from a set of electric meters that are associated with anelectric grid; analyzing, by the processor, the data to determine afirst set of time intervals with voltage variances that satisfy avoltage threshold condition; analyzing, by the processor, the data todetermine a second set of time intervals that include electricconsumption that satisfy a consumption threshold condition; andcomparing, by the processor, the first set of time intervals and thesecond set of time intervals to identify whether the set of electricmeters are associated with non-technical line loss, wherein thenon-technical line loss is detected when the first set of time intervalsof the voltage variances do not match at least a threshold percentage ofthe second set of time intervals of the electric consumption within adefined margin.
 17. The computer-implemented method of claim 16, whereinthe data from the utility database specifies electric usage informationover a period of time that is divided into a plurality of timeintervals.
 18. The computer-implemented method of claim 17, furthercomprising: sorting the plurality of time intervals according toelectric consumption to determine which of the plurality of timeintervals satisfy the consumption threshold condition, wherein theconsumption threshold condition is satisfied when a time interval of theplurality of time intervals is ranked in a top decile for electricconsumption among the plurality of time intervals.
 19. Thecomputer-implemented method of claim 17, further comprising: determiningvoltage variances between a voltage for each of the set of meters at theplurality of time intervals and a respective reference voltageassociated with each of the plurality of time intervals.
 20. Thecomputer-implemented method of claim 17, further comprising: determiningelectric consumption for the set of electric meters at each of theplurality of time intervals by summing a total electric consumption forthe set of meters during respective intervals of the plurality of timeintervals.